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PubMed2003

Modeling and implementing a health information provider on the Internet.

OBJECTIVES: To model and implement a medical web portal providing access to well qualified and high-quality information. MATERIAL AND METHODS: The Unified Medical Language System (UMLS) knowledge sources of the U.S. National Library of Medicine and an Information Sources Catalog (ISC) resulting from the ARIANE project are described. The XML technology that allows files transformations by the means of XSLT is briefly presented. RESULTS: The design and implementation of software modules that exploit knowledge sources, operate the translation of a user's query to selected information sources, and wrap obtained results are detailed. DISCUSSION: Current implementation and wrapping perspectives are discussed in terms of integration and interoperability of health information resources.

Humans↗

PubMed2003

The UMLS Semantic Network and the Semantic Web.

The Unified Medical Language System is an extensive source of biomedical knowledge developed and maintained by the US National Library of Medicine (NLM) and is being currently used in a wide variety of biomedical applications. The Semantic Network, a component of the UMLS is a structured description of core biomedical knowledge consisting of well defined semantic types and relationships between them. We investigate the expressiveness of DAML+OIL, a markup language proposed for ontologies on the Semantic Web, for representing the knowledge contained in the Semantic Network. Requirements specific to the Semantic Network, such as polymorphic relationships and blocking relationship inheritance are discussed and approaches to represent these in DAML+OIL are presented. Finally, conclusions are presented along with a discussion of ongoing and future work.

Programming Languages↗

PubMed2003

IndexFinder: a method of extracting key concepts from clinical texts for indexing.

Extracting key concepts from clinical texts for indexing is an important task in implementing a medical digital library. Several methods are proposed for mapping free text into standard terms defined by the Unified Medical Language System (UMLS). For example, natural language processing techniques are used to map identified noun phrases into concepts. They are, however, not appropriate for real time applications. Therefore, in this paper, we present a new algorithm for generating all valid UMLS concepts by permuting the set of words in the input text and then filtering out the irrelevant concepts via syntactic and semantic filtering. We have implemented the algorithm as a web-based service that provides a search interface for researchers and computer programs. Our preliminary experiment shows that the algorithm is effective at discovering relevant UMLS concepts while achieving a throughput of 43K bytes of text per second. The tool can extract key concepts from clinical texts for indexing.

Abstracting and Indexing↗

PubMed1992

The UMLS coverage of clinical radiology.

The informational content of clinical radiology reports was examined to determine the coverage of the Unified Medical Language System (UMLS) in relation to the terminology used by physicians in the Radiology Department of Columbia Presbyterian Medical Center (CPMC). The UMLS semantic network contained 17 semantic types which were compatible with the types of clinical information in the reports. The type of semantic categories missing from the UMLS consisted mainly of modifier information relating to certainty, degree, and change type of information. This type of information formed a substantial part of the domain. Although most of the informational categories were found in the UMLS semantic network, most of the domain terms were not. Our results strongly suggest that the UMLS could be a significant tool for developing clinical text processing applications if it were extended to cover clinical domains.

Evaluation Studies as Topic↗

PubMed1999

Modeling the UMLS using an OODB.

The Unified Medical Language System combines many well established authoritative medical informatics terminologies in one system. Such a resource is very valuable to the healthcare industry. However, the UMLS is very large and complex and poses serious comprehension problems for users and maintenance personnel. Furthermore, the sets of concepts of semantic types are not semantically uniform and thus are difficult to study. We describe a method to represent two components of the UMLS, the Metathesaurus (META) and the Semantic Network, as an OODB. The resulting UMLS OODB schema is deeper and more refined than the Semantic Network. It offers semantically uniform classes, which improves support for comprehension and navigation of META. The UMLS OODB also exposes problems in the semantic type classifications.

Classification↗

PubMed2002

Enriching the structure of the UMLS semantic network.

The Unified Medical Language System's (UMLS's) Semantic Network (SN)---consisting of a network of semantic types---has a two-tree structure, where each semantic type has at most one parent semantic type. This arrangement is restrictive because some semantic types are, by their definition, specializations of several parents. As a proposed enhancement to the SN, its semantic types have previously been partitioned into groups, each of which contains semantic types of some specific area. However, some groups of this proposed partition contain forest (i.e., multiple-tree) structures or even isolated semantic types. Both situations imply a disconnected internal structure. Connectivity is actually one way to assess the proposed "semantic validity" principle for partitions. It is a desired, although not required, property. In this paper, we introduce a methodology for identifying "missing" IS-A links and adding them to the SN. This process transforms the SN into a Directed Acyclic Graph (DAG) structure, with semantic types permitted to have multiple parents. A result of our methodology is the transformation of the proposed SN partition into groups satisfying the connectivity property.

Semantics↗

PubMed2003

Method for automatic management of the semantic network ambiguity in the UMLS: possible application for information retrieval on the Web.

The Unified Medical Language System (UMLS) is an extensive collection of terms and concepts. The UMLS includes biomedical terms from standard classifications. The semantic network (SN) links the concepts, sometimes ambiguously. In this paper we try, on one hand to describe the relationship between concepts more efficiently and on the other hand to find new relationships. Assuming that re-usability and automatic extraction of knowledge from existing thesaurus enables an improvement of the metatheasaurus, we cross the SN with linked concepts from the ADM (Assisted Medical Diagnosis). Results are presented and our discussion concerns firstly the use of the SN only; secondly the improvement that allows pre-selection of linked concepts, and thirdly the possibility to coincide with other developments that improve the metathesaurus.

Electronic Data Processing↗

PubMed1993

Generic queries for meeting clinical information needs.

This paper describes a model for automated information retrieval in which questions posed by clinical users are analyzed to establish common syntactic and semantic patterns. The patterns are used to develop a set of general-purpose questions called generic queries. These generic queries are used in responding to specific clinical information needs. Users select generic queries in one of two ways. The user may type in questions, which are then analyzed, using natural language processing techniques, to identify the most relevant generic query; or the user may indicate patient data of interest and then pick one of several potentially relevant questions. Once the query and medical concepts have been determined, an information source is selected automatically, a retrieval strategy is composed and executed, and the results are sorted and filtered for presentation to the user. This work makes extensive use of the National Library of Medicine's Unified Medical Language System (UMLS): medical concepts are derived from the Metathesaurus, medical queries are based on semantic relations drawn from the UMLS Semantic Network, and automated source selection makes use of the Information Sources Map. The paper describes research currently under way to implement this model and reports on experience and results to date.

Algorithms↗

PubMed1996

Methodology for using the UMLS as a background knowledge for the description of surgical procedures.

The Unified Medical Language System (UMLS) contains and organizes a large number of terms from a variety of biomedical terminology systems. This study examines the relevance of the UMLS content and structures to the specific purpose of the conceptual representation of medical procedures. The MAOUSSC modelling is a compositional formalism with a description of elementary procedures in terms of elementary concept entities and combinations of such descriptions into more complex ones. The UMLS knowledge base is expected to provide semantically categorized medical concepts and interconcept relations. A method to reuse the UMLS has been developed. Quantitative and qualitative results are presented. Some difficulties in reusing the UMLS as a background knowledge are related to the preeminence of some terminology sources and to the instanciation of interconcept links. Other ones suggest that purpose-independence in categorization cannot be achieved.

Artificial Intelligence↗

PubMed1991

From meaning to term: semantic locality in the UMLS Metathesaurus.

The Unified Medical Language System Metathesaurus represents the results of a synthesis of existing biomedical naming systems (thesauri). The naming and other information about the meanings in the Metathesaurus can be used to find the preferred naming of that meaning in the source chosen by the user, by exploiting the property of semantic locality. The aspects of semantic locality in the Metathesaurus which can be thus exploited are the terms, the semantic types, the use of that term in a source context, and the co-occurrence of terms in MEDLINE. To find how a meaning is named in the source of choice, a user must exploit one of these aspects of semantic locality, entering a term somehow related to the term being sought, and navigating to the preferred term. While the first three of these aspects of semantic locality are normative, the last is empirical. Testing of the utility of the aspects of semantic locality in information retrieval would require a uniform interface with 1, no Metathesaurus, 2, the Metathesaurus without the aspects in question, and 3, the Metathesaurus including all the aspects. Other potential uses of empirically derived semantic locality include defining or suggesting potentially relevant concepts in a given situation.

Semantics↗

PubMed1992

Interactive query workstation: a demonstration of the practical use of UMLS knowledge sources.

The Interactive Query Workstation (IQW) has been developed to provide clinicians with a uniform program interface for retrieving medical-related information from various computer-based information resources. These resources can vary in content (bibliographic databases, drug information, general medical text databases), function (article retrieval, differential diagnosis, drug interaction detection, or drug dosage and administration information), and media formats (local hard disk, CD-ROM, local area network, or distant telecommunication link). IQW allows modular addition of new resources as well as extension of previously installed resources. The National Library of Medicine's three Unified Medical Language System (UMLS) Knowledge Sources, the Metathesaurus (Meta), the Semantic Network, and the Information Sources Map (ISM) have been incorporated into many aspects of IQW. Meta provides information about medical terminology and aids IQW in isolating the basic concepts from a clinician's question. The Semantic Network provides information about the categorization of concepts and possible relations between concepts. It also assists IQW in determining which queries are appropriate for a set of concepts contained in the clinician's question. The ISM provides information about the content available from a computer-based resources and aids IQW in selecting an appropriate resource from which to collect information. The computer-based resource selection is performed without user intervention. This interactive demonstration shows an environment which increases the accessibility of medical information to clinicians by utilizing the three UMLS Knowledge Sources.

Information Storage and Retrieval↗

PubMed2002

The completeness of existing lexicons for representing radiology report information.

Although most medical lexicons contain up to 80% of clinical terms used in an ambulatory patient medical records archive, preliminary research suggests that they may be far less complete for radiology terms. We therefore compared the likelihood that several existing medical lexicons would contain terms found in a radiology report to the likelihood they would contain terms found in an ambulatory care medical record. We used three samples of imaging terms to assess the completeness of existing lexicons for medical imaging: (1) a random sample of imaging terms from the Unified Medical Language System Large Scale Vocabulary Test (UMLS-LSVT; n = 218), (2) terms actually used in the first 80 clinical knee magnetic resonance imaging reports generated by the routine clinical use of a structured reporting system (eDictation, Marlton, NJ; n = 76), and (3) terms listed in a glossary of thoracic imaging prepared by the Fleischner Society (n = 173). Using the UMLS Web-based Knowledge Source Server (http://umlsks.nlm.nih. gov/), we measured the rate at which terms in each of the above three sources were found in the UMLS and two of its major constituent terminologies: ICD-9-CM and SNOMED International. ICD-9-CM contained matches for 3%, 8%, and 11% of terms from the Fleischner Society Glossary, eDictation, and NLM-LSVT, respectively. SNOMED International contained matches for 32%, 46%, and 36% of terms from the Fleischner Society Glossary, eDictation, and NLM-LSVT, respectively. The UMLS contained matches for 36%, 50%, and 45% of terms from the Fleischner Society Glossary, eDictation, and NLM-LSVT, respectively. The assessed vocabularies were least likely to contain a term from the Fleischner Society Glossary and most likely to contain a term from the eDictation lexicon. The UMLS was the most complete, and ICD-9 was the least complete of the three systems evaluated. No lexicon achieved greater than 50% completeness for any test set of imaging terms. Our results show that no single lexicon is sufficiently complete to allow comprehensive indexing, search, and retrieval of radiology report information. These results confirm the few results available from the medical literature indicating that existing controlled vocabularies are insufficiently complete to represent the contents of radiology reports. A subjective analysis of these results may identify particular imaging sub-areas for which new terms should be developed.

Humans↗

PubMed2005

Relationship structures and semantic type assignments of the UMLS Enriched Semantic Network.

OBJECTIVE: The Enriched Semantic Network (ESN) was introduced as an extension of the Unified Medical Language System (UMLS) Semantic Network (SN). Its multiple subsumption configuration and concomitant multiple inheritance make the ESN's relationship structures and semantic type assignments different from those of the SN. A technique for deriving the relationship structures of the ESN's semantic types and an automated technique for deriving the ESN's semantic type assignments from those of the SN are presented. DESIGN: The technique to derive the ESN's relationship structures finds all newly inherited relationships in the ESN. All such relationships are audited for semantic validity, and the blocking mechanism is used to block invalid relationships. The mapping technique to derive the ESN's semantic type assignments uses current SN semantic type assignments and preserves nonredundant categorizations, while preventing new redundant categorizations. RESULTS: Among the 426 newly inherited relationships, 326 are deemed valid. Seven blockings are applied to avoid inheritance of the 100 invalid relationships. Sixteen semantic types have different relationship structures in the ESN as compared to those in the SN. The mapping of semantic type assignments from the SN to the ESN avoids the generation of 26,950 redundant categorizations. The resulting ESN contains 138 semantic types, 149 IS-A links, 7,303 relationships, and 1,013,876 semantic type assignments. CONCLUSION: The ESN's multiple inheritance provides more complete relationship structures than in the SN. The ESN's semantic type assignments avoid the existing redundant categorizations appearing in the SN and prevent new ones that might arise due to multiple parents. Compared to the SN, the ESN provides a more accurate unifying semantic abstraction of the UMLS Metathesaurus.

Semantics↗

PubMed2000

Graphical tool for navigation within the semantic network of the UMLS metathesaurus on a locally installed database.

Knowledge in the environment of information technologies is bound to structured vocabularies. Medical data dictionaries are necessary for uniquely describing findings like diagnoses, procedures or functions. Therefore we decided to locally install a version of the Unified Medical Language System (UMLS) of the U.S. National Library of Medicine as a repository for defining entries of a medical multimedia database. Because of the requirement to extend the vocabulary in concepts and relations between existing concepts a graphical tool for appending new items to the database has been developed: Although the database is an instance of a semantic network the focus on single entries offers the opportunity of reducing the net to a tree within this detail. Based on the graph theorem, there are definitions of nodes of concepts and nodes of knowledge. The UMLS additionally offers the specification of sub-relations, which can be represented, too. Using this view it is possible to manage these 1:n-Relations in a simple tree view. On this background an explorer like graphical user interface has been realised to add new concepts and define new relationships between those and existing entries for adapting the UMLS for specific purposes such as describing medical multimedia objects.

Computer Graphics↗

PubMed2000

Using UMLS semantics for classification purposes.

The Unified Medical Language System (UMLS) contains semantic information about terms from various sources; each concept can be understood and located by its relationships to other concepts. We describe a method in which the semantic relationships between UMLS concepts are exploited for the purpose of classification. This method combines three existing components: 1) Mapping terms to UMLS concepts; 2) Restricting UMLS concepts to MeSH; and 3) Mapping MeSH terms to disease categories. When applied to the automatic classification of condition terms into broad disease categories in the Clinical Trials database, this method assigned relevant categories to 92% of the 1823 condition terms encountered. 135 (7%) failed to be classified and 14 (.77%) were misclassified. The limits of this method are discussed, as well as the reuse of existing components, and the tuning required to achieve automatic classification.

Algorithms↗

PubMed2004

Failure analysis of MetaMap Transfer (MMTx).

A pilot study was conducted to evaluate the performance of the MetaMap Transfer (MMTx), a tool that extracts terms from free text and suggests matches to concepts in the Unified Medical Language System' (UMLS'). Five participants, including a content domain expert and a UMLS Expert, manually extracted and mapped terms to UMLS concepts for two disease summary documents from NLM's consumer health site, Genetic Home Reference. The resulting adjudicated annotations were used as a gold standard. Differences in auto-mated term extraction and mapping between MMTx and MetaMap were noted. A failure analysis was conducted to categorize the types of terms not correctly mapped by MMTx. The most frequent type of failure (30%) resulted from missing inferential or world knowledge. Characteristics of each category are discussed. We distinguish between classes of failures that may be easily rectified, such as alternative retrieval strategies to extract exact matches, and ones that require additional research, such as coordinating conjunctions, co-reference resolution, and word sense disambiguation

Abstracting and Indexing↗

PubMed1991

Adding your terms and relationships to the UMLS Metathesaurus.

The National Library of Medicine's Unified Medical Language System [1] Metathesaurus contains the richest single corpus of biomedical names in existence. Yet, developers wishing to make use of the Metathesaurus will be confronted by users who want to add local terminology and relationships not already represented there. We urge developers to fill those needs, while, at the same time, they plan for the many consequences of unilateral Metathesaurus enhancement. Foremost among these consequences is the need to maintain local enhancements across subsequent releases of the Metathesaurus. These problems are illustrated via examples of candidate Metathesaurus enhancement terms in use at the Columbia-Presbyterian Medical Center (CPMC), at the Mayo Clinic, and in Current Disease Descriptions (CDD). Sharing and reuse of Metathesaurus enhancement methods may permit local enhancements to be used at other sites, and it may permit the global Metathesaurus utilization effort to benefit from economies of scale.

Terminology as Topic↗

PubMed2005

Indexing anatomical phrases in neuro-radiology reports to the UMLS 2005AA.

This work describes a methodology to index anatomical phrases to the 2005AA release of the Unified Medical Language System (UMLS). A phrase chunking tool based on Natural Language Processing (NLP) was developed to identify semantically coherent phrases within medical reports. Using this phrase chunker, a set of 2,551 unique anatomical phrases was extracted from brain radiology reports. These phrases were mapped to the 2005AA release of the UMLS using a vector space model. Precision for the task of indexing unique phrases was 0.87.

Abstracting and Indexing↗